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A comparison of marker gene selection methods for single-cell RNA sequencing data
Open accessAbstract
The development of single-cell RNA sequencing (scRNA-seq) has enabled scientists to catalogue and probe the transcriptional heterogeneity of individual cells in unprecedented detail. A common step in the analysis of scRNA-seq data is the selection of so-called marker genes, most commonly to enable annotation of the biological cell types present in the sample. In this paper we benchmarked 56 computational methods for selecting marker genes in scRNA-seq data. The performance of the methods was compared using 10 real scRNA-seq datasets and over 170 additional simulated datasets. Methods were compared on their ability to recover simulated and expert-annotated marker genes, the predictive performance and characteristics of the gene sets they select, their memory usage and speed and their implementation quality. In addition, various case studies were used to scrutinise the most commonly used methods, highlighting issues and inconsistencies. Overall, we present a comprehensive evaluation of methods for selecting marker genes in scRNA-seq data. Our results highlight the efficacy of simple methods, especially the Wilcoxon rank-sum test, Student’s t-test and logistic regression. All code used in the evaluation, including an extensible Snakemake pipeline, is available at: https://gitlab.svi.edu.au/biocellgen-public/mage_2020_marker-gene-benchmarking .
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“For this purpose, Wilcoxon rank-sum tests performed best, but owing to the nature of clustering, P values can be inflated and might lead to false discoveries, as the same data are used to define the labels that we test for differences between60,61.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [60]
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“For this purpose, Wilcoxon rank-sum tests performed best, but owing to the nature of clustering, P values can be inflated and might lead to false discoveries, as the same data are used to define the labels that we test for differences between60,61.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [60]
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Metadata
- Authors
- Pullin, J. M., & McCarthy, D. J.
- Affiliations
- The University of Melbourne (AU)
- Corresponding author
- McCarthy, Davis J.
- Venue
- Genome biology
- Publisher
- openRxiv
- Published
- 2022-05-10
- Volume
- 25
- Issue
- 1
- Pages
- 56-56
- ISSN
- 1465-6906 (ISSN-L)
- PMC
- PMC10895860
- OpenAlex
- W4392167979
- Cited by
- 122
- References
- 72
- Citations by year
- 2026: 43 · 2025: 46 · 2024: 23 · 2023: 10
- FWCI
- 15.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Baker Foundation (GNT1162829)
- University of Melbourne
- Medical Research Council
- National Health and Medical Research Council (1162829, 1112681, 1195595)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- marker gene selection, single-cell RNA sequencing, benchmarking, cell type annotation, Mann-Whitney test, Student's t-test, logistic regression, simulated data, computational performance
- MeSH terms
- Exome Sequencing, Software, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
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